3D LiDAR Point Cloud Loop Detection Based on Dynamic Object Removal

Abstract

The 3D point cloud accumulates laser scans at different locations and times. Since laser scanning captures a snapshot of surrounding environment, moving objects are often observed and contained now and then. The dynamic objects in the point cloud map can degrade the quality of the map and affect the localization accuracy, thus it is critical to remove the dynamic objects from the 3D point cloud map. In this paper, a baseline based on dynamic object removal for 3d point cloud loop detection is proposed. To eliminate the interference of moving objects in the environment. First, the radar point cloud data is preprocessed, namely, the 3D object detection model OpenPCDet is employed to detect dynamic objects in the outdoor scene, such as vehicles, pedestrians, etc. Second, we use the bounding box detected by the model to perform cube filtering on the original data to remove dynamics objects. Finally, the processed data is utilized to extract scene descriptors for loop detection. In the road scene, experimental results demonstrate that our approach yields superior performance against the traditional methods.